
math-basics-for-ai
Math basics course materials
Stars: 64

This repository provides resources and materials for learning fundamental mathematical concepts essential for artificial intelligence, including linear algebra, calculus, and LaTeX. It includes lecture notes, video playlists, books, and practical sessions to help users grasp key concepts. The repository aims to equip individuals with the necessary mathematical foundation to excel in machine learning and AI-related fields.
README:
- Lecturer: Evgeniya Korneva
- Pre-recorder video lectures: see group chat.
- Live practical sessions: Wednesdays & Fridays 19:00 Moscow time. Recordings are uploaded afterwards.
- Office hours: upon request
- (course) Topics in Linear Algebra: lecture notes + quizes.
- (Youtube playlist) Linear Algebra for Engineers: a series of videos covering the most important concepts.
- (lecture notes) Linear Algebra in 25 Lectures (UC Davis)
- (book) Introduction to Applied Linear Algebra
- (book) Deep Learning - Part I
- (Youtube playlist) Essence of Calculus
- (lecture notes) Introduction to Differential Calculus [pdf]
- (lecture notes) First Semester Calculus [pdf]
- Learn LaTeX in 30 minutes – an Overleaf guide
- A series of great YouTube tutorials:
- Detexify - draw a symbol you are looking for, and this web will give you its latex representation.
- FINAL EXAM [pdf]LaTeX template][submission form]
- Deadline: Friday, January 24, 18:59 Moscow time
- Graded assignmnet 4 [pdf][LaTeX template][submission form]
- Deadline: Monday, October 21, 23:59 Moscow time
- Graded assignmnet 3 [pdf][notebook (task 2)][LaTeX template][submission form]
- Deadline: Sunday, October 6, 23:59 Moscow time
- Graded assignment 2 [notebook][submission form]
- Deadline: Sunday, September 29, 23:59 Moscow time
- Graded assignment 1 [pdf] [LaTex template][submission form]
- Deadline: Friday, September 20, 18:59 Moscow time
- Welcome quiz [google form]
- Vectors - Pyhton practice:
- Homework:
- watch lectures 1 & 2 (see chat);
- lecture 1 quiz [google form] (not graded).
- Getting familiar with LaTeX:
- Review lecture 2
- Gram-Schmidt process [notebook][solutions]
- Homework:
- Quiz lectures 1 - 3 [google form]
- Quiz review
- Method of least squares
- Python practice [notebook]
- Homework
- watch lecture 4
- graded assignment 1 (deadline Wednesday, September 18, before the class)
- Method of least squares continued
- Homework:
- Quiz: [google form]
- Review quiz lectuures 1-4
- LU, QR and Eigendecompositions
- Homework:
- graded assignment 2 (deadline Sunday, September 29, 23:59 Moscow time)
- Review PCA notebook
- SVD
- Homework:
- graded assignment 3 (deadline Sunday, October 6, 23:59 Moscow time)
- SVD Python practice [notebook]
- watch lecture 6
- Quiz: [google form]
- Univariate functions
- Multivariate functions
- Matrix calculus
- Homework:
- graded assignment 4 (deadline Monday, October 21, 23:59 Moscow time)
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